Meta-Cognitive Regulation Might Be the Most Important AI Skill Nobody Is Talking About

元认知调节可能是目前无人提及的最重要的 AI 技能

Artificial Intelligence Meta-Cognitive Regulation Might Be the Most Important AI Skill Nobody Is Talking About. As AI gets smarter, the real differentiator may be how well humans regulate their own thinking. 人工智能元认知调节可能是目前无人提及的最重要的 AI 技能。随着人工智能变得越来越聪明，真正的人才差异化因素可能在于人类调节自身思维的能力。

We all have been leaning into the world of generative AI adoption for almost the past three years now. We’ve spent the last three years learning how to talk to AI, but what if I told you that the next big shift will be learning how not to let AI think for us?! 在过去的三年里，我们都在积极拥抱生成式人工智能。我们花了三年时间学习如何与 AI 对话，但如果我告诉你，下一个重大转变将是学习如何不让 AI 代替我们思考呢？！

With the growing exposure of AI in our personal and professional lives, and as I talk to my peers, industry leaders, and experts about the skills that matter the most today around AI, I hear one word the most—prompting. Prompting is now considered a basic foundational skill for effective AI interaction. 随着 AI 在我们个人和职业生活中日益普及，当我与同行、行业领袖和专家探讨当今 AI 领域最重要的技能时，我听到最多的一个词就是“提示词工程”（Prompting）。提示词工程现在被认为是有效进行 AI 交互的基本功。

We have moved on from the strategy of adopting generative AI in everyday work to creating “conversational” partnerships between humans and AI agents that are precise, contextual, and goal-oriented. And this partnership is essential for bridging the gap between high-level human intent and valuable, actionable AI output. 我们已经从在日常工作中采用生成式 AI 的策略，转向在人类与 AI 智能体之间建立精准、情境化且目标导向的“对话式”伙伴关系。这种伙伴关系对于弥合人类高层意图与有价值、可执行的 AI 输出之间的鸿沟至关重要。

All that to say, the people getting the most value from AI aren’t the best prompters; they’re the ones actively regulating their thinking while they use it! This group doesn’t just think with AI—they actively think about how they’re thinking while using AI. And this skill may quietly become the defining human advantage in the AI era. That skill is: metacognitive regulation. 总而言之，从 AI 中获得最大价值的人并不是最擅长写提示词的人，而是那些在使用 AI 时积极调节自身思维的人！这一群体不仅是与 AI 一起思考，他们还在使用 AI 的同时，积极审视自己的思考方式。这种技能可能会悄然成为 AI 时代人类的决定性优势。这项技能就是：元认知调节。

What Is Metacognition, Really?

究竟什么是元认知？

Metacognition is “thinking about your own thinking”. It is the awareness of your thoughts and the ability to control, monitor, and adjust to your own thinking in pursuit of a goal. Since this whole new horizon of human-AI interaction has opened up in front of us, I have been reading a lot about concepts in psychology and cognitive science, which is where I learned about metacognition. 元认知就是“对思维的思考”。它是对自身思想的觉察，以及在追求目标的过程中控制、监控和调整自己思维的能力。自从人类与 AI 交互的全新视野在我们面前展开以来，我阅读了大量心理学和认知科学的概念，并由此了解到了元认知。

Metacognition is an internal human system that notices when you’re rushing, when you’re overconfident, when you’re emotionally attached to an idea, when your reasoning has gaps, or when you’ve accepted an answer simply because it sounded convincing. And now, this is about to become incredibly important in the AI-driven world we live in! 元认知是人类的一种内在系统，它能察觉到你何时在仓促行事、何时过于自信、何时对某个想法产生了情感依赖、何时推理存在漏洞，或者何时仅仅因为答案听起来有说服力就盲目接受。在当今这个由 AI 驱动的世界里，这一点正变得极其重要！

Think about this: when was the last time you had an original thought and you pursued it without consulting the internet? The large language models of today are extraordinarily good at producing outputs that feel complete even when they are shallow, a little wrong, or subtly narrow your thinking, all without you noticing. 试想一下：你上一次产生原创想法并深入探究，且没有查阅互联网是什么时候？当今的大型语言模型非常擅长生成看起来很完整的内容，即使这些内容可能很肤浅、存在细微错误，或者在你不经意间狭隘了你的思维。

This is where metacognitive regulation becomes essential. The strongest AI users with their metacognition constantly monitor: whether they actually understand the output, whether they agree with it, whether they’re being intellectually lazy, whether AI is expanding their reasoning or replacing their own creative thought. This self-awareness is going to be the real differentiator in the AI skillset that I feel nobody is talking about right now. 这就是元认知调节变得至关重要的地方。最强大的 AI 用户会利用元认知不断监控：他们是否真正理解了输出结果，是否认同这些结果，自己是否存在思维懒惰，以及 AI 是在扩展他们的推理能力还是在取代他们自己的创造性思维。这种自我觉察将成为 AI 技能组合中真正的差异化因素，而我觉得目前还没人谈论这一点。

The Difference Between AI Users and AI Thinkers

AI 使用者与 AI 思考者的区别

As my organization and I work with AI adoption in my 9-5, or talking to peers in conferences and meetups, I sense that something interesting is emerging: while most of the workforce today is using AI agents passively and/or outsourcing thinking in exchange for speed, a much smaller group of people is using AI differently. 在我的日常工作中推动 AI 应用，或在会议和聚会上与同行交流时，我感觉到一种有趣的现象正在出现：虽然当今大多数职场人士都在被动地使用 AI 智能体，或者为了追求速度而将思考过程外包，但有一小部分人正在以不同的方式使用 AI。

These users aren’t asking AI to replace reasoning but instead, they are using AI agents to stress-test, expand, organize, or challenge their own personal reasoning. Instead of saying “give me the answer to problem x”, these smart AI users ask: What assumptions am I missing? What would invalidate my argument? Can you critique my logic? What perspective have I ignored? Why does this conclusion feel incomplete? 这些用户不是要求 AI 取代推理，而是利用 AI 智能体来压力测试、扩展、整理或挑战他们个人的推理过程。这些聪明的 AI 用户不会说“给我问题 X 的答案”，而是会问：我遗漏了哪些假设？什么会推翻我的论点？你能批评我的逻辑吗？我忽略了什么视角？为什么这个结论感觉不完整？

In the next few months, your fluency with AI will not directly correlate to your technical capabilities, but I see it increasingly becoming a test of cognitive awareness. AI today doesn’t just automate work; it is here to change cognition. In one of my last posts, I wrote that one of the most under-discussed aspects of Generative AI is that it does not merely accelerate tasks, it reshapes habits. 在接下来的几个月里，你对 AI 的熟练程度将不再直接等同于你的技术能力，我发现它正日益成为对认知觉察力的考验。今天的 AI 不仅仅是自动化工作，它旨在改变认知。我在之前的一篇文章中写道，生成式 AI 最少被讨论的方面之一是：它不仅加速了任务，还在重塑我们的习惯。

So What Does A Metacognitive AI User Look Like?

那么，一个具备元认知的 AI 用户是什么样的？

Metacognitive regulation is not about becoming better at prompting. It’s about being more intentional about your own thinking while working with AI. The best AI users don’t blindly optimize for speed and output—they stay mentally present. They know when to pause, question, challenge, refine, and think independently. 元认知调节并不是要让你更擅长写提示词，而是要在与 AI 合作时，对自己的思维过程更加审慎。最优秀的 AI 用户不会盲目地追求速度和产出，他们保持思维的在场感。他们知道何时该停下来、质疑、挑战、提炼并进行独立思考。

I’ll give you an example – Before (A typical AI user): “Summarize this report and give recommendations.” After (A metacognitive user): “Summarize this report, and tell me what assumptions you’re making, where the data might mislead me, and what conclusions would not be justified.” 我举个例子： 之前（典型的 AI 用户）：“总结这份报告并给出建议。” 之后（具备元认知的用户）：“总结这份报告，并告诉我你做了哪些假设，数据在哪些地方可能会误导我，以及哪些结论是不合理的。”

Becoming truly fluent with AI means resisting the urge to outsource every difficult cognitive moment. Here’s what that looks like in practice: 真正精通 AI 意味着要抵制将每一个困难的认知时刻都外包出去的冲动。在实践中，这表现为：

• Challenge AI outputs: AI can prematurely close the loop on thinking if left unquestioned. I say, challenge the output produced by the AI agent more. Come up with contradictions to that output, and remember that the fastest answer isn’t always the most correct. 挑战 AI 的输出： 如果不加质疑，AI 可能会过早地终结思考过程。我认为，应该更多地挑战 AI 智能体生成的输出。尝试提出与该输出相矛盾的观点，并记住：最快的答案并不总是最正确的。

• Sit with uncertainty long enough to develop an original thought: As humans, we do not really like discomfort, confusion, and iteration. And thanks to the AI agents, you can have multiple perspectives on a business question within seconds. But metacognitive users resist that urge and sit with ideas long enough to form their own perspective. 在不确定性中停留足够长的时间以形成原创想法： 作为人类，我们并不喜欢不适感、困惑和反复迭代。多亏了 AI 智能体，你可以在几秒钟内获得关于商业问题的多种视角。但具备元认知的用户会抵制这种（急于求成）的冲动，并在想法中停留足够长的时间，从而形成自己的观点。

• Hold competing ideas simultaneously: AI can generate a code with 400 lines or a wireframe for a dashboard in seconds, but thoughtful users evaluate them instead of rushing to resolution. I love when my work has nuances because that leads to me thinking of the grey area and working through it. 同时持有相互竞争的想法： AI 可以在几秒钟内生成 400 行代码或仪表板的线框图，但深思熟虑的用户会评估它们，而不是急于得出结论。我喜欢我的工作包含细微差别，因为这会引导我思考灰色地带并深入钻研。